CENTER SPECIFIC IMPLANTATION VOLUMES - A PREDICTOR OF CLINICAL OUTCOMES WITH MECHANICAL CIRCULATORY SUPPORT?
Bibliographic record
Abstract
PURPOSE: This study examined center specific implantation volumes in relation to clinical outcomes with mechanical circulatory support. METHODS: Utilizing the Novacor LVAS Global Registry, the study cohort consisted of 1293 patients at 86 centres which were grouped by implant volume; 1–10 implants (n=193, 55 centers), 11–25 (n=199, 12 centers), 26–50 (n=394, 12 centers), and over 51 (n=507, 7 centers). RESULTS: Center volumes of >10 implants was a predictor of favorable outcome, odds ratio of 1.733 (95% confidence interval 1.274–2.357, p <0.001). In centers with volumes of <10 implants just 89 of 193 patients achieved a favorable outcome (i.e. transplantation/weaning). Significant differences were noted between this group and all other groups; 11–25 implants (119/199 transplant/weaned, p=0.007), 26–50 (238/394 transplant/weaned, p <0.001) and over 51 (300/507 transplant/weaned, p=0.002). To assess if these findings were simply a function of volume based learning, composite results from the first 10 implants of each center performing more than 10 implants was compared with the group with less than 10 implants. This comparison also resulted in significance (191/309 versus 89/193 transplant/weaned, p=0.001) indicating centres with larger volumes had superior results even during the early patient experience (first 10 implants). CONCLUSIONS: Center specific volumes can impact clinical outcomes. The centers performing low volumes (<10 implants) were demonstrated to have worst results. Factors such as implantation frequency, experience with other devices, etc. may also be potentially responsible for these results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".